library('sf')
## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUE
library('tmap')
library('tmaptools')
library('tidyverse')
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6      ✔ purrr   0.3.4 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library('here')
## here() starts at C:/Users/diana/CASA/Term1/0005_GIS/Practical/week5/wk5exam2/w4pratical
library(countrycode)
## Warning: package 'countrycode' was built under R version 4.2.2
#install.packages("countrycode")
#package country code installed, hence removed it from the code segment as it 
#repeatedly prompted restart while attempting to knit on save.
world_shape <- st_read(here("World_Countries_(Generalized)","World_Countries__Generalized_.shp"))
## Reading layer `World_Countries__Generalized_' from data source 
##   `C:\Users\diana\CASA\Term1\0005_GIS\Practical\week5\wk5exam2\w4pratical\World_Countries_(Generalized)\World_Countries__Generalized_.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 251 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -89 xmax: 180 ymax: 83.6236
## Geodetic CRS:  WGS 84
gdi <- read_csv(here("gdi.csv"))
## New names:
## Rows: 206 Columns: 7
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (4): iso3, country, hdicode, region dbl (2): gdi_2010, gdi_2019 lgl (1): ...7
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...7`
gdi <- gdi %>%
  mutate(diff=gdi_2019-gdi_2010)
#gdi <- gdi %>%
#  rename(ISO=iso3)
#countrycode('Albania', 'country.name', 'iso3c')

gdi1 <- gdi %>%
  mutate(code= countrycode(country,'country.name', 'iso3c'))
## Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Arab States, East Asia and the Pacific, Europe and Central Asia, High human development, Latin America and the Caribbean, Low human development, Medium human development, South Asia, Sub-Saharan Africa, Very high human development, World
world_shape1 <- world_shape %>%
  mutate(code= countrycode(COUNTRY,'country.name', 'iso3c'))
## Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: Azores, Bonaire, Canarias, Glorioso Islands, Juan De Nova Island, Madeira, Micronesia, Saba, Saint Eustatius, Saint Martin, Turkiye
shape <- left_join(world_shape1, gdi1, by = "code")
#plot
library(tmap)
tmap_mode("plot")
## tmap mode set to plotting
# change the fill to your column name if different
shape %>%
  qtm(.,fill = "diff")
## Variable(s) "diff" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.

print(shape)
## Simple feature collection with 361 features and 16 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -180 ymin: -89 xmax: 180 ymax: 83.6236
## Geodetic CRS:  WGS 84
## First 10 features:
##    FID             COUNTRY ISO          COUNTRYAFF AFF_ISO   SHAPE_Leng
## 1    1         Afghanistan  AF         Afghanistan      AF   50.8032097
## 2    2             Albania  AL             Albania      AL    9.6250380
## 3    3             Algeria  DZ             Algeria      DZ   70.5400822
## 4    4      American Samoa  AS       United States      US    0.6001244
## 5    5             Andorra  AD             Andorra      AD    0.9373518
## 6    6              Angola  AO              Angola      AO   58.8734698
## 7    7            Anguilla  AI      United Kingdom      GB    0.4683887
## 8    8          Antarctica  AQ                <NA>    <NA> 1564.1441426
## 9    9 Antigua and Barbuda  AG Antigua and Barbuda      AG    1.1629014
## 10  10           Argentina  AR           Argentina      AR  138.7513483
##      SHAPE_Area code iso3             country   hdicode region gdi_2010
## 1  6.256671e+01  AFG  AFG         Afghanistan       Low     SA    0.627
## 2  3.073540e+00  ALB  ALB             Albania      High    ECA    0.960
## 3  2.135135e+02  DZA  DZA             Algeria      High     AS    0.838
## 4  1.371972e-02  ASM <NA>                <NA>      <NA>   <NA>       NA
## 5  5.558517e-02  AND  AND             Andorra Very High   <NA>       NA
## 6  1.038131e+02  AGO  AGO              Angola    Medium    SSA    0.867
## 7  7.797911e-03  AIA <NA>                <NA>      <NA>   <NA>       NA
## 8  5.672928e+03  ATA <NA>                <NA>      <NA>   <NA>       NA
## 9  4.562595e-02  ATG  ATG Antigua and Barbuda      High    LAC       NA
## 10 2.783090e+02  ARG  ARG           Argentina Very High    LAC    0.995
##    gdi_2019 ...7  diff                       geometry
## 1     0.712   NA 0.085 MULTIPOLYGON (((61.27655 35...
## 2     1.002   NA 0.042 MULTIPOLYGON (((19.57083 41...
## 3     0.880   NA 0.042 MULTIPOLYGON (((4.603354 36...
## 4        NA   NA    NA MULTIPOLYGON (((-170.7439 -...
## 5        NA   NA    NA MULTIPOLYGON (((1.445836 42...
## 6     0.902   NA 0.035 MULTIPOLYGON (((23.47611 -1...
## 7        NA   NA    NA MULTIPOLYGON (((-63.16778 1...
## 8        NA   NA    NA MULTIPOLYGON (((-180 -84.30...
## 9        NA   NA    NA MULTIPOLYGON (((-61.73806 1...
## 10    1.000   NA 0.005 MULTIPOLYGON (((-71.85916 -...

Plot the Gender Inequality Difference 2010 & 2019 map_for wk4Exam

tmap_mode("plot")
## tmap mode set to plotting
breaks=c(-0.6,-0.03,-0.01, 0.01, 1, 50, 100, 150, 200) 

tm1 <- tm_shape(shape) + 
  tm_polygons("gdi_2010",
              breaks=breaks,
               palette="PuOr")+
  tm_legend(show=FALSE)+
  tm_layout(frame=FALSE)+
  tm_credits("(a1: gii_2019)", position=c(0,0.85), size=1.5)


tm2 <- tm_shape(shape) + 
  tm_polygons("gdi_2019", 
               breaks=breaks,
             palette="PuOr")+
  tm_legend(show=FALSE)+
  tm_layout(frame=FALSE)+
  tm_credits("(a2: gii_2010)", position=c(0,0.85), size=1.5)


tm3 <- tm_shape(shape) + 
  tm_polygons("diff", 
               breaks=breaks, 
              palette="PuOr")+
  tm_legend(show=FALSE)+
  tm_layout(frame=FALSE)+
  tm_credits("(a3: gii_rank_2021)", position=c(0,0.85), size=1.5)


legend <- tm_shape(shape) +
    tm_polygons("diff",
                 breaks=breaks,midpoint = NA,
               palette="PuOr") +
    tm_scale_bar(position=c(-0.4,0.8), text.size=0.6)+
    tm_compass(north=0, position=c(0.65,0.6))+
    tm_layout(legend.only = TRUE, legend.position=c(0.2,0.25),asp=0.1)+
    tm_credits("Gender Inequality Difference 2010 & 2019", position=c(0.0,0.0))
  
t=tmap_arrange(tm1, tm2, tm3, legend, ncol=2)
print(t)
## Variable(s) "diff" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.
## Scale bar set for latitude km and will be different at the top and bottom of the map.

tmap_mode("view")
## tmap mode set to interactive viewing
breaks=c(-0.6,-0.03,-0.01, 0.01, 1)
tm_shape(shape) + 
  tm_polygons("diff", breaks=breaks, midpoint = NA)

```